SAR provides excellent renditions of Earth surface, and its image quality is almost independent of weather conditions. Among several civilian SAR systems, the RADARSAT SAR has a unique capability to offer multiple beam modes, resolutions, and incidence angles. The fine beam mode of the RADARSAT SAR is specifically useful to monitor fine detailed surface features because of its high resolution. However, speckle noise in SAR image, produced by coherent illumination on rough surface, degrades quality of image and often leads to misclassification. Since it is single-look image, speckle noise in fine beam mode is normally more serious than that in standard beam mode. To reduce speckle in the fine beam mode image of RADARSAT SAR, we have developed a speckle reduction approach by adopting a weight averaging process in wavelet transform domain. The test results demonstrate how effectively the approach reduces the speckle noise in a fine beam mode image, and its performance is discussed in terms of three criteria:1) preservation of mean, 2) reduction of variance, and 3) preservation of edges. To optimize the algorithms with respect to level of wavelet transform and weight, further tests are, however, necessary.

Random noise is often a problem in 2-D digital data sets since it obscures fine detail and makes identification of image features difficult. Adaptive filters have now been used routinely to suppress speckle(random) noise in Synthetic Aperture Radar(SAR) images. SAR data is similar to seismic reflection data both in their processing steps and in their final format. Therefore these adaptive filters can be effectively applied to reducing random noise in both seismic data and SAR data. In this study some popular adaptive filters: Lee filter, Frost filter, and Kuan filter, are tested on LITOPROBE(AGT) Sudbury seismic reflection data to determine if background random noise can be suppressed while reflection events are enhanced. The performance of these filters is also tested with RADARSAT data. The adaptive filters successfully suppressed random noise in the LITHOPROBE seismic reflection section while keeping blurring to a minimum. The enhanced Lee filter performed best closely followed by the enhanced Frost filter and the Kuan filter. In filtering the SAR image all three filters worked well at eliminating random noise, including the speckle noise. The enhanced Lee and enhanced Frost filter worked about equally well while the Kuan filter caused slightly more blurring on the image.

A new synoptic approach is presented to analyze the sea surface flow field based on coupling two kinds of quantitative information: current vectors derived from sequential AVHRR images and geometrical parameters of objects such as frontal currents, eddies and streamers delineated on these images. The synoptic approach is developed to yield the better estimates of the maximal flow speeds in the two different case studies in the East Sea and Kuroshio Extension area. The satellite-derived velocity profiles were examined against in situ ADCP, CTD, drifter and altimeter data and we proves that the combined synoptic approach of satellite imagery is successful in overcoming the major drawback of underestimated speeds. Discovery of streamers in the East Sea that is hardly capturable by conventional field measurements demonstrates further benefits of the developed approach.

Spectral characteristics of rice canopies were treated using spectroradiometer to examine the feasibility of satellite remote sensing for the monitoring of crop condition related to leaf nitrogen content. We measured spectral reflectance, leaf chlorophyll amount, and leaf nitrogen content of rice canopies at each development state. LANDSAT TM equivalent band set was created by averaging measured spectral reflectance values to the real TM band range. Chlorophyll amount was highly significant in the visible ranged TM equivalent band and in the biband ratios of NIR(TM4) and VIS(TM3, TM2, TM1) region. In addition, leaf chlorophyll amount was closely related to leaf nitrogen content and therefore applied to estimate the leaf nitrogen content in rice canopies.

An automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples is presented. Also, the performance of our method has been quantitatively assessed on a multispectral SPOT image and compared to those of 1-nearest neighbor classifier and quadratic Gaussian classifier. The method is based on adaptive multi-scale feature space partitioning. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy subspaces are further iteratively partitioned if the prespecified classification performance has not been archived on the training set. The advantages of our method are: 1) the tracking of highly non-linear decision hypersurfaces, 2) resolving the conflicts in patterns due to the inheritage of the fuzzy rule-based method. As a result, our classifier can achieve high performances in highly non-linear pattern classification problem. Our classifier was trained and tested on patterns consisting of the DN of each band, (XS1, XS2, XS3), extracted from SPOT multispectral scene. The training and testing was prepared by experts on the analysis of satellite images. Our method archived 95.84 % classification accuracy, while 1-nearest neighborhood classifier 92.09 %, quadratic Gaussian classifier 91.31%. The result represents that our method has higher generalization power.

In this paper, a hybrid algorithm is proposed to extract 3D information from stereo aerial image. To reduce the errors in depth discontinuity, multiple directional windows constrained by an edge map extracted from the images, were used. The matching algorithm has two steps which are edge pixel and non-edge pixel matching. The edge pixels are matched using adaptive window that varies its shape according to the direction of edge. The non-edge pixels are matched using multiple directional windows constrained by an edge map. Postprocessing is performed to remove mismatched points and interpolate unmatched points. Then the results of edge and non-edge matching are merged. The images used in experiments are real aerial stereo images. Experimental results show that the proposed matching algorithm reduces mismatch points and improves the result at the depth discontinuity comparing with the conventional matching algorithm.

Electromagnetic wave scattering from the randomly rough sea surfaces was computed by using the Monte Carlo method and the Method of Moment. A relationship between the wind speed and the surface roughness parameters are obtained empirically at first. Then, a nonlinear directional randomly rough sea surface was generated for a given wind speed. This directional rough surface shows both vertical and horizontal skewness. An impedance boundary condition was used in the computation of the electric fields scattered from the nonlinear directional rough surfaces. A tapered impedance sheet was used to reduce the edge effect to improve the computation of the scattering coefficient at large incidence angles. It was shown that the computation result for sea surface scattering agreed well with the new PO model.

Optical remote sensor data obtained over mountainous area show significant tonal variation due to the different solar illumination on relief slopes. In this paper, three methods of radiometric correction to reduce the terrain effects are tested for two satellite imagery obtained in optical spectrum. Landsat TM and SPOPT HRV data obtained over the mountainous forest area were initially registered to a plane rectangular coordinate system, along with digital elevation model(DEM) data having comparable spatial resolutions with the satellite imagery. Using the solar angles (zenith and azimuth) at the time of data acquisition and the topographic slope and aspect angles for each pixel location, effective local incidence angle between the sun and the surface normal to terrain slope was calculated to analyze and to correct the radiometric distortions. Several fields of rather homogeneous forest stands were delineated over the imagery using digitized forest stand map and the relationship between pixel values and the local incidence angle were analyzed. After the effects of incidence angle on the pixel brightness values were defined, the radiometric correction was performed. Among the three methods tested, the Minnaert correction provide the most reliable result for normalizing the terrain effects on the imagery. The parameterization to apply the Minnaert correction are presented for two different regions of mountainous forests in Korea.

The study was to develop integrated Geo-Information System software package called KGIS(Kigam Geo-Information System) with the newly created Kigam Integrated Format(KID) format. KGIS software package developed by KIGAM is for the geological information management system that is designed to work with data referenced by spatial or geographic coordinates. The system is capable of handling spatial geoscience data such as geological survey data, thematic maps of the geological map, hydrogeological map, geochemistry map and geophysical map, images, ore deposits, profile map, reports and other geo-information. The software to be developed is mainly focused on the user-friendly GUI interface and for the people who don't have much background on the field of remote sensing and GIS. The system runs on windows environment and the platforms under X-windows based is now undergoing. The main functions of KGIS are as follows : 1) Display/Import, 2) Export, 3) Edit, 4) Mosaic, 5) Overlay, 6) Convert, 7) Attribute, 8) Database, 9) Image processing, 10) Output

The purpose of this paper is to develop a traffic data collection method for managing and controlling flow rates in urban bridge areas using geographic information systems (GIS) and a digitized panchromatic imagery acquired from Russian satellite. The existing traffic monitoring systems including manual and automatic vehicle recording technologies have diverse limitations in terms of accuracies and spatial coverage ranges. The 2 meter resolution of remotely sensed data and GIS are integrated and simulated by frequency transformation.